How to forecast multiple target attributes in Python?
In general, multi output models is not that different.
I.e.

As Raghu mentioned in commentary, you could train separate model for each output. There is even helper module in sklearn for that (MultiOutputRegressor)
DecisionTreeRegressor from sklearn allows multiple outputs out-of-the box
Any neural network framework allows any number of outputs

In your particular case there are two much bigger factors.

It's timeseries. Timeseries requires whole another set of models then other data, i.e ANOVA, RNN or transformers.
You data is not well scaled. The reason is that the change between points is insignificant in comparison with their absolute values. You need to deal with it -- but exact solution would depend on your particular task. You could try to substract the mean value on train set or predict the difference, not absolute value.

Edit: here is simple example
import numpy as np
raw = np.array([
[414049364,21773560],
[414049656,21773926],
[414049938,21774287],
[414050204,21774638],
[414050453,21774975],
[414050682,21775296],
[414050895,21775597],
[414051093,21775874],
[414051278,21776125],
[414051453,21776344],
[414051620,21776530],
[414051780,21776678],
[414051935,21776785],
[414052089,21776849],
[414052242,21776865]
])

X = raw[:-1]
y = raw[1:]
from sklearn.linear_model import LinearRegression
model = LinearRegression()
# Train the model
model.fit(X, y)
# Try to predict next step
model.predict([[414051780,21776678]])
# The predicted value [414051933,  21776806] and the true value would be [414051935,21776785], not so far


Next you could normalize the values, use few previous steps as a features, use more complex algorithm then linear regression, and make train/eval split for better model evaluation.